Measuring Self-Preferencing on Digital Platforms

Abstract

Digital platforms facilitate exchanges between platform actors, such as trading between buyers and sellers. However, providers of digital platforms also act on those platforms as direct competitors of third parties. Therefore, an inappropriate behavior, referred to as self-preferencing, could occur, i.e., treating the platform’s own offers better than comparable third-party offers. However, detecting self-preferencing is challenging. This article addresses this challenge conceptually and empirically. First, it puts forward a conceptual framework that defines self-preferencing and conceptualizes two self-preferencing tests. It further develops a six-step approach to implement this framework. This approach includes a novel metric of a product’s visibility on a platform, uniquely suited for self-preferencing tests. To illustrate the approach, this article applies it in two empirical studies across three international Amazon marketplaces. The results find only modest evidence for self-preferencing. However, implementing these tests requires researchers to choose between several alternative empirical choices and assumptions—all reasonable, but each can potentially affect the results. The approach proposed herein includes extensive sensitivity, specification curve, and heterogeneity analyses, which provide means to systematically assess the robustness of the conclusions—and show that the main results are not driven by those choices.

Keywords: Digital Platforms, Electronic Commerce, Amazon, Competition, Search Engines

JEL Classification: D47, D18, D12, D83, D40, L81, L86, L13, K21, M31, M21